Cross-validating models of continuous data from simulation and experiment by using linear regression and artificial neural networks

Zakeri, Z. ORCID: 0000-0003-2588-8360, Mansfield, N. ORCID: 0000-0001-6769-1721, Sunderland, C. ORCID: 0000-0001-7484-1345 and Omurtag, A. ORCID: 0000-0002-3773-8506, 2020. Cross-validating models of continuous data from simulation and experiment by using linear regression and artificial neural networks. Informatics in Medicine Unlocked, 21: 100457.

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Abstract

We are increasingly surrounded by sensors gathering massive amounts of data, and patterns in continuous variables are often discovered by using artificial neural networks (ANN), while linear regression (LR) is useful for detecting linear relationships. LR also provide preliminary estimates of potentially complex associations, and serve as a benchmark for the performance of ANNs. We show that while cross-validation (CV) is indispensable for insuring the robustness of the discovered patterns, it systematically leads, when combined with LR, to specific artefacts that underestimate the extent of the associations between predictor and target variables. We explain how this previously unnoticed type of artefact arises specifically from the combination of CV with LR and does not affect non-linear methods such as ANN. We also demonstrate through simulations that ANN were able to discover a wide range of complex associations missed by LR. The results were confirmed by the analysis of physiological, behavioural and subjective data collected from N=31 human subjects performing laparoscopy training experiments.

Item Type: Journal article
Publication Title: Informatics in Medicine Unlocked
Creators: Zakeri, Z., Mansfield, N., Sunderland, C. and Omurtag, A.
Publisher: Elsevier
Date: 2020
Volume: 21
Identifiers:
NumberType
10.1016/j.imu.2020.100457DOI
S2352914820306079Publisher Item Identifier
1389382Other
Rights: © 2020 Published by Elsevier Ltd. This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 12 Jan 2021 12:33
Last Modified: 12 Jan 2021 12:33
URI: http://irep.ntu.ac.uk/id/eprint/42016

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